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Alain Clément

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Alain Clément

Riccardo Soliani

Professore di seconda fascia
Dipartimento di Scienze Politiche
Universitá degli Studi di Genova

Je me souviens quand jai rencontré la première fois Alain à un Colloque de lAssociation Charles Gide. Nous avions parlé de mon exposé, qui portait sur un auteur du dix-huitième siècle, lors dune pause café, comme cela arrive souvent. Nous avons été immédiatement daccord sur beaucoup de choses. Du coup, nous avons eu une entente et une compréhension aussi culturelle que personnelle : une amitié est née spontanément.

Je me souviens de la dernière fois quAlain avait été à Gênes, une ville quil aimait beaucoup. Fini les activités éducatives et scientifiques, on adorait de trouver un jour pour une petite randonnée. Cette fois, on avait décidé de faire un voyage à lintérieur, entre la Ligurie et le Piémont. Javais lui dit quil ny avait pas de grandes œuvres dart, ou de lieux dattraction naturelle. Il répondit : « Mais jaime aussi voir comment les gens vivent. » Dans cette phrase, il y avait tout Alain. Ses activités de recherche et denseignement ont été nourries de sa profonde humanité. Il a été en mesure de transmettre aux étudiants, des contenus raffinés avec facilité, comme je lai vu faire à la fois à Tours et à Gênes ; et les étudiants lappréciaient et laimaient pour cela. Lintérêt pour léconomie sociale, déjà mûri dans la période quil avait passée en Afrique, était naturel en lui et cétait tout à fait bien visible, lorsquil étudiait soit le dix-huitième siècle, soit une période plus près de nous.

Voilà pourquoi jai choisi de présenter, dans cet hommage, un texte quAlain avait écrit avec moi et Enrico Ivaldi, un collègue et ami de 22Gênes, expert en méthodes statistiques, qui lui aussi était devenu ami dAlain. Nous avions tous les trois la volonté dessayer de comprendre « comment les gens vivent » en Italie et en France. Nos compétences différentes se sont facilement rejointes et il sen suivi un papier paru il y a un peu plus dun an dans la International Review of Social Sciences [The full version of the article “Social and Material Deprivation in French and Italian Macro Regions: A Proposal of New Indicators from Eu-Silc Data”, by Alain Clément, Enrico Ivaldi and Riccardo Soliani, has been published on the International Review of Social Sciences, vol. 3, Issue 9, September 2015, open access]. Cet article a servi de base pour les dernières leçons que nous avons faites ensemble à Gênes, dans le cadre du projet Erasmus.

Je propose ici de présenter très brièvement, sans notes ni bibliographie, les principaux points de ce travail et une esquisse de sa genèse. Il rappelle un aspect peut-être moins bien connu de lactivité intense de lami Alain Clément.

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Souvenir de Alain Clément, co-auteur de Social and Material Deprivation in French and Italian Macro Regions: A Proposal of New Indicators from Eu-Silc Data, avec Enrico Ivaldi et Riccardo Soliani.

Many elements allow the comparison based upon the respective importance of material and social elements of deprivation in France and Italy: population, Gross Domestic Product (GDP) per capita in Purchasing Power Parity (PPS) and rate of growth are very similar in the last decade. The sustainability of the welfare state is a major challenge in both countries. Also life expectancy and rate of mortality are almost the same in France and Italy.

Thus a little group of three French and Italian friends, researchers interested in different fields of study (social economics, history of economic and social thought, statistics), but, overall, in the words of one of them, “into peoples way of life”, decided to study the social and material deprivation in France and Italy.

The availability of Eurostat data EU-SILC enabled us to adopt macro-regions as geographical scale, to single out better the (dis)similarities between the countries (Table 2a-b). After a conversation in a nice restaurant in Tours, where I was in Erasmus mobility, several calls France-Italy and a discussion to which Alain participated via e-mail, we adopted the methodology suggested by Enrico Ivaldi, the statistician of the team: factorial analysis. Thanks to it (and to him!), we selected six variables that explained about the 65 % of variability between the macro-regions and we made two indexes: the Material Condition Index (MCI) and the Social Condition Index (SCI). Our final step was the aggregation between MCI and SCI, which gave the the Social and Material Condition Index (SMCI).

The aggregation (really not simple!) was, again, a task for Enrico. All of us were involved in a long, passionated debate about the results.

The variables are the following: the first group of three are in the MCI, the remaining in the SCI:

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Gross domestic product (GDP) at current market prices

Employment by sex and age from 25 to 64 years

Income of households

Not Single-parent household

Persons aged 25-64 with higher secondary education attainment

Households with access to the Internet at home

Tab. 1 – Variables description.

Île-de-France

Île-de-France

Parisian basin

Champagne-Ardenne, Picardie, Haute-Normandie, Centre, Basse-Normandie, Bourgogne

Nord-Pas-de-Calais

Nord-Pas-de-Calais

East

Lorraine, Alsace, Franche-Comté

West

Pays de la Loire, Bretagne, Poitou-Charentes

South West

Aquitaine, Midi-Pyrénées, Limousin

Centre East

Rhône-Alpes, Auvergne

Mediterranean

Languedoc-Roussillon, Provence-Alpes-Côte dAzur, Corse

Tab. 2a – Classification (France).

North West

Valle dAosta, Liguria, Lombardia, Piemonte

North East

Emilia-Romagna, Friuli-Venezia Giulia, Trentino-Alto Adige/Südtirol, Veneto

Centre

Lazio, Marche, Toscana, Umbria

South

Abruzzo, Puglia, Basilicata, Calabria, Campania, Molise

Islands

Sardegna, Sicilia

Tab. 2b – Classification (Italy).

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We based our analysis on a set of variables in line with most of literature: on this point Alain enriched the work very much with information and reflection, always in his simple style, always offering significant contribution in a friendly way. Serge Paugam, member of the Observatoire Sociologique du Changement, was a key reference he knew very well. Paugam founded his analysis of the “spiral of precariousness” on indicators such as job, family structure, poverty, social relations. Indeed Paugams “precariousness” is synonymous of our material and social “deprivation”: as poverty, as social deprivation are likely to trigger a vicious circle leading to “social disqualification”, that is unemployment, dependency, lack of self-esteem.

But much more emerged from our conversations in Tours or in Genoa, very frequently sipping a coffee. In France public authorities (the Haut commissaire aux solidarités actives contre la pauvreté), unsatisfied of the monetary definition of poverty were setting up complementary indicators of poverty, which consider education, health, home, employment and so on. Even Michel Foucault, whose methodological approach was very different from ours, in a lecture in 1970 held in Tokyo put forward three forms of exclusion, or “existential marginality”, concerning the poor: job, family and language, that roughly correspond to our data on employment, one parent families, and education and Internet access.

Generally speaking, our groups of variables describe the individual and social condition of people. On the one hand, GDP and employment are standard macroeconomic indicators of the level of economic activity and the effectiveness of the system in creating jobs; the income of households has the prominent role regarding the material deprivation. On the other hand, single-parent households indicate a situation that is relevant to quantify social deprivation, and they are much more at risk of unemployment, as shown by long-period data.

In the material index, the labour market trends play a key role. Here the emerging difference deserves some further reflection, and Enrico and me remember of the long discussions in Genoa with Alain, at the Department or at the coffee after lessons: it is due to structural elements and to the reactions to the current crisis as well (see Cochard and Heyer 2010). In 2009 in France the rate of activity (70.7 %) and employment (64.2 %) were significantly higher than in Italy (62.4 % and 57.5 % respectively), and have recently risen; the difference in the rate of female 26employment is remarkable: 60.1 % compared with 46.4 %. However the upturn of the active population and the crisis have increased the rate of unemployment, which is 9.1 % (in Italy 6.9 %), with a significant share of long term unemployment. In France the drop in GDP and employment, caused by the crisis, has been lower than in Italy, but at the same time the rate of unemployment is higher, so mirroring the different structure of the labour market, namely the lower rate of participation in Italy.

All this, together with the economic policies to cope with the crisis, also influences poverty. In France political action is more effective than in Italy: the rate of poverty in France is 25 % before and 13 % after social transfers; in Italy the data are 24 % and 20 % respectively. Poverty affects the two countries almost in the same measure, but the French social net expenditure is more efficacious in reducing it. Since the beginning of the crisis, the upturn of the subsidies to the unemployed (+10.8 %) and the extension of the public aid (+22.7 %) has tackled the deterioration of the labour market and the risk of poverty. In 2009 the global social aid Prestations de protection sociale was 36.7 % of the gross disposable income of households (34.7 % in 2000; 32.8 % in 1990).

As we have seen, we have chosen the cited six variables from Eurostat, among about forty of those affecting socio-economic conditions, to give quantitative evidence to these phenomena. Exploratory factorial analysis on the selected variables has been carried out to detect if we can individuate two subsets of variables, each one reflecting a latent data dimension. In fact social and material variables are significantly loaded on the first and on the second factor respectively, thus empirically validating the conceptual distinction. All of us were satisfied, because the distinction between “social” and “material” could be tested and held well.

The classification is reported in Table 3.

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SCI

MCI

SMCI

Île-de-France

(F)

1.9

5.9

7.8

North West

(I)

-0.1

3.9

3.8

North East

(I)

0.6

2.4

3.1

Centre

(I)

0.7

1.5

2.2

Centre East

(F)

1.1

0.0

1.1

West

(F)

1.0

-0.7

0.3

Parisian basin

(F)

0.2

0.0

0.1

South West

(F)

0.7

-0.9

-0.2

East

(F)

0.6

-1.4

-0.8

Mediterranean

(F)

-0.7

-1.0

-1.7

Nord-Pas-de-Calais (F)

-0.8

-2.7

-3.5

South

(I)

-2.3

-2.7

-5.0

Islands

(I)

-2.9

-4.2

-7.1

Tab. 3 – Social, Material and Social-Material Conditions Indexes.

The final step is grouping the values of indexes into categories to identify the areas (classes) with similar socio-economic conditions. The distribution of the index has been divided into four classes: class 1 identifies the countries with the best conditions, while class 4 contains, on the contrary, countries characterized by the lowest index value (Tables 5a-c). The patterns appear quite different, suggesting a theoretical distinction between material and social deprivation.

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Class

Country

1

Île-de-France (F)

2

North East (I), Centre (I), Centre East (F), West (F), Parisian basin (F), South West (F), East (F),

3

North West (I) Mediterranean (F), Nord-Pas-de-Calais (F)

4

South (I), Islands (I)

Tab. 4a – Classes homogeneous with respect to the SCI index.

Class

Country

1

Île-de-France (F), North West (I)

2

North East (I), Centre (I), Centre East (F), Parisian basin (F)

3

West (F), South West (F) Mediterranean (F), East (F), South (I), Nord-Pas-de-Calais (F)

4

Islands (I)

Tab. 4b – Classes homogeneous with respect to the MCI index.

Class

Country

1

Île-de-France (F), North West (I)

2

North East (I), Centre (I), Centre East (F), West (F), Parisian basin (F)

3

South West (F), East (F), Mediterranean (F), Nord-Pas-de-Calais (F)

4

South (I), Islands (I)

Tab. 4c – Classes homogeneous with respect to the SMCI index.

At this point, we have the elements to cast some light on deprivation in France and Italy. The way of life of people, that is the raison dêtre of social economy, and also, in my opinion, shared by Alain and Enrico, of political economy itself, has been quantified and analysed from a particular standpoint. We cannot state that we have the key to explain poverty and inequality; but we provide a quantitative analysis, without any preliminary model, which should help understanding the phenomena. I remember the relaxed and friendly long conversations 29with Enrico and Alain, who was really happy to deal with an issue he knew very well from an original and new point of view. Indeed, curiosity and sincere will to go further in research were between his more attractive qualities. He had also the rare gift of expounding easily and with nonchalance various issues that were quite difficult: we could see and appreciate it. The gist of our debate is as follows.

General demand-sided macroeconomic policies financed by progressive taxation can improve material conditions. On the other side, social deprivation must be faced by fine-tuned public policies (e.g.: education and access to the web) aiming at the equality of capabilities and/or “endowments”, or by specific welfare programs. The structural spatial features emerged must be taken into account together with the metropolitan polarizing effects. Such effects contribute to the overall good score of some regions, but must be governed, to avoid the imbalances that polarisation/depolarisation may engender.

From the geographical distribution of deprivation, two conclusions emerge: first, inequality in Italy is stronger even on territorial basis, with the wide gap of Southern Italy and the Islands; secondly, the performance of the Île-de-France, where the geographical polarization is to be considered as the aftermath of the role of Paris. Our results can be compared with some regional demographic trends, showing interesting analogies. In France the higher demographic rise takes place in the Southern and Western regions, where also the balance of immigration is positive (inflow higher than outflow); on the contrary, it is negative for the North and the North-East. The Île-de-France has net outflow of population, but its dwellers are particularly young: presumably Paris attracts the young, while old Parisiens prefer to move towards smaller towns or to the countryside. In Italy, where the natural demographic trend is negative, the small rise in the population (about 0.5 %), due to immigration, is higher in the North and also the Centre, thanks to the attractiveness of Rome.

In long, stimulating conversations around three cups of tea or coffee, Alain Clément discussed with us the concrete dimension of these results, remembering his studies and personal experience. Thanks to his warm sense of humanity and his deep knowledge, never exhibited, our tables of data became living people.